C-Reactive Protein, Fecal Calprotectin, and Stool Lactoferrin for Detection of Endoscopic Activity in Symptomatic Inflammatory Bowel Disease Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: Persistent disease activity is associated with a poor prognosis in inflammatory bowel disease (IBD). Therefore, monitoring of patients with intent to suppress subclinical inflammation has emerged as a treatment concept. As endoscopic monitoring is invasive and resource intensive, identification of valid markers of disease activity is a priority. The objective was to evaluate the diagnostic accuracy of C-reactive protein (CRP), fecal calprotectin (FC), and stool lactoferrin (SL) for assessment of endoscopically defined disease activity in IBD. METHODS: Databases were searched from inception to November 6, 2014 for relevant cohort and case-control studies that evaluated the diagnostic accuracy of CRP, FC, or SL and used endoscopy as a gold standard in patients with symptoms consistent with active IBD. Sensitivities and specificities were pooled to generate operating property estimates for each test using a bivariate diagnostic meta-analysis. RESULTS: Nineteen studies (n=2499 patients) were eligible. The pooled sensitivity and specificity estimates for CRP, FC, and SL were 0.49 (95% confidence interval (CI) 0.34-0.64) and 0.92 (95% CI 0.72-0.96), 0.88 (95% CI 0.84-0.90) and 0.73 (95% CI 0.66-0.79), and 0.82 (95% CI 0.73-0.88) and 0.79 (95% CI 0.62-0.89), respectively. FC was more sensitive than CRP in both diseases and was more sensitive in ulcerative colitis than Crohn's disease. CONCLUSIONS: Although CRP, FC, and SL are useful biomarkers, their value in managing individual patients must be considered in specific clinical contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".